Machine learning for track reconstruction at the LHC

The planned upgrade of the LHC to its High-Luminosity counterpart (HL-LHC) circa 2027 will bring about a drastic increase in instantaneous luminosity, pileup, and trigger rates. Currently, most LHC experiments use Kalman filter based track reconstruction algorithms which exhibit outstanding physics...

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Bibliographic Details
Published in:Journal of instrumentation 2022-02, Vol.17 (2), p.C02026
Main Author: Gagnon, L.-G.
Format: Article
Language:English
Subjects:
Analysis and statistical methods
Data processing methods
Particle tracking detectors
Pattern recognition, cluster finding, calibration and fitting methods
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Summary:The planned upgrade of the LHC to its High-Luminosity counterpart (HL-LHC) circa 2027 will bring about a drastic increase in instantaneous luminosity, pileup, and trigger rates. Currently, most LHC experiments use Kalman filter based track reconstruction algorithms which exhibit outstanding physics performance but scale poorly with the amount of data produced per bunch crossing. Therefore, the high energy physics community is currently performing intensive R&D to commission new or improved algorithms for this crucial data reconstruction task. This article presents many approaches such as running existing Kalman filter algorithms on accelerated hardware and overhauling the current approaches with machine learning techniques. A new algorithm testbed for research in track reconstruction, ACTS, is also discussed.
ISSN:1748-0221
1748-0221
DOI:10.1088/1748-0221/17/02/C02026